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1. News and Geolocated Social Media Accurately Measure Protest Size Variation

   https://doi.org/10.1017/S0003055420000295  

   SOBOLEV, ANTON; CHEN, M. KEITH; JOO, JUNGSEOCK; STEINERT-THRELKELD, ZACHARY C. (November 2020, American Political Science Review)

   null (Ed.)

   Larger protests are more likely to lead to policy changes than small ones are, but whether or not attendance estimates provided in news or generated from social media are biased is an open question. This letter closes the question: news and geolocated social media data generate accurate estimates of protest size variation. This claim is substantiated using cellphone location data from more than 10 million individuals during the 2017 United States Women’s March protests. These cellphone estimates correlate strongly with those provided in news media as well as three size estimates generated using geolocated tweets, one text-based and two based on images. Inferences about protest attendance from these estimates match others’ findings about the Women’s March. 

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2. CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media

   https://doi.org/10.1177/0081175019860244  

   Zhang, Han; Pan, Jennifer (July 2019, Sociological Methodology)

   Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)—a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China). We evaluate the performance of CASM through cross-validation, out-of-sample validation, and comparisons with other protest data sets. We assess the effect of online censorship and find it does not substantially limit our identification of events. Compared to other protest data sets, CASM-China identifies relatively more rural, land-related protests and relatively few collective action events related to ethnic and religious conflict. 

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3. MMCHIVED: Multimodal Chile and Venezuela Protest Event Data

   https://doi.org/10.1609/icwsm.v16i1.19385  

   Steinert-Threlkeld, Zachary; Joo, Jungseock (June 2022, Proceedings of the International AAAI Conference on Web and Social Media)

   This paper introduces the Multimodal Chile & Venezuela Protest Event Dataset (MMCHIVED). MMCHIVED contains city-day event data using a new source of data, text and images shared on social media. These data enables the improved measurement of theoretically important variables such as protest size, protester and state violence, protester demographics, and emotions. In Venezuela, MMCHIVED records many more protests than existing datasets. In Chile, it records slightly more events than the Armed Conflict Location and Events Dataset (ACLED). These extra events are from small cities far from Caracas and Santiago, an improvement of coverage over datasets that rely on newspapers, and the paper confirms they are true positives. While MMCHIVED covers protest events in Chile and Venezuela, the approach used in the paper is generalizable and could generate protest event data in 107 countries containing 97.14% of global GDP and 82.7% of the world's population. 

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4. Black Protests in the United States, 1994 to 2010

   https://doi.org/10.15195/v9.a12  

   Oliver, Pamela; Lim, Chaeyoon; Matthews, Morgan; Hanna, Alex (January 2022, Sociological Science)

   Using novel data, we provide the first panoramic view of U.S. Black movement protest events as reported in U.S. newswires between 1994 and 2010 and put our quantitative data into dialogue with qualitative accounts. Struggles during these years presaged the Black Lives protest waves of 2014 to 2016 and 2020. Protests increased after the 1995 Million Man March into 2001 but dropped abruptly after the 9/11 attacks. Collective action increased again at the end of the 2000s. Protests in response to police violence and other criminal-legal issues were major arenas of struggle and news coverage. Also common were issues of national identity including celebrations of Black history and Black solidarity, protests about Confederate symbols, and protests about White hate groups and hate crimes. Although Black people protested about a wide variety of issues, newswires focused disproportionately on incidents of police violence and perceived threats of Black violence. There is substantial continuity in issues, organizations, and activism between this earlier period and the Black Lives Movement of 2014 to 2020. 

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5. Predicting Protests and Riots in Urban Environments With Satellite Imagery and Deep Learning

   Warnke, Scott; Runfola, Dan (July 2024, Transactions in GIS)

   Conflict, manifesting as riots and protests, is a common occurrence in urban environments worldwide. Understanding their likely locations is crucial to policymakers, who may (for example) seek to provide overseas travelers with guidance on safe areas, or local policymakers with the ability to pre-position medical aid or police presences to mediate negative impacts associated with riot events. Past efforts to forecast these events have focused on the use of news and social media, restricting applicability to areas with available data. This study utilizes a ResNet convolutional neural network and high-resolution satellite imagery to estimate the spatial distribution of riots or protests within urban environments. At a global scale (N = 18,631 conflict events), by training our model to understand relationships between urban form and riot events, we are able to predict the likelihood that a given urban area will experience a riot or protest with accuracy as high as 97%. This research has the potential to improve our ability to forecast and understand the relationship between urban form and conflict events, even in data-sparse regions. 

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